The engine logs say CUDA graphs are disabled for your deployment. Does it matter?

Measure exposed launch and dispatch delay, actual graph coverage, capture memory and warmup. Batch size influences the result, but does not alone determine whether graphs improve the service.

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Measure exposed launch and dispatch delay, actual graph coverage, capture memory and warmup. Batch size influences the result, but does not alone determine whether graphs improve the service.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

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⚡ Kernels & Compilers🔒 Premium

torch.compile and CUDA Graphstorch.compile optimizes captured computation, while CUDA graphs reduce repeated launch overhead. Both need validation against the model’s shapes, control flow and memory behavior.

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🚀 Inference & ServingSign in

Continuous BatchingContinuous batching changes which requests run between model iterations. Learn how a waiting request joins, why an empty slot is not enough, and how to balance throughput against each user’s token latency.

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🧮 Napkin Math & Capacity🔒 Premium

Bandwidth-Bound Decode ThroughputEstimate decode throughput from bytes per step, then separate aggregate output from per-user pace. A finite-memory example shows how batching, cache reads and quantization change the bound.

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🚀 Inference & Serving🔒 Premium

The Serving Optimization WaterfallTune an LLM deployment one change at a time against one fixed workload, in an order that follows the binding constraint. A measured run went from 789 to 6,568 output tokens per second, and the two biggest wins were not the knobs people usually reach for first.

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UP NEXT ON YOUR JOURNEY

Next in this trackFifty teams have fine-tuned the same base model. How do you serve all of them?Next in this trackA replica restarts and loads different weights than its siblings. How did that happen?Next in this trackYou must serve a frontier open-weights model on non-NVIDIA accelerators. Plan it.

FEDITOR'S NOTE

Scored on critical-path rather than summed CPU overhead, full versus partial capture, runtime shape coverage and measured latency/capacity tradeoffs.

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